Effect of CO2 on Rock Properties: Frio Crosswell Case Study

2015 ◽  
Author(s):  
M. Al-Hosni ◽  
S. Vialle ◽  
B. Gurevich ◽  
T. Daley
Keyword(s):  
Rock Properties

scholarly journals Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Nilgün Güdük ◽  
Miguel de la Varga ◽  
Janne Kaukolinna ◽  
Florian Wellmann
Keyword(s):  
Structural Parameters ◽  
Rock Properties ◽  
Magnetic Data ◽  
Geological Model ◽  
Borehole Data ◽  
Likelihood Functions ◽  
Different Types ◽  
Geophysical Information ◽  
Different Sources

Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework.

scholarly journals Comparison between electrical resistivity tomography and tunnel seismic prediction 303 methods for detecting the water zone ahead of the tunnel face: A case study

2020 ◽  
Vol 12 (1) ◽  
pp. 1094-1104
Author(s):  
Nima Dastanboo ◽  
Xiao-Qing Li ◽  
Hamed Gharibdoost
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Geological Structure ◽  
Rock Properties ◽  
Electrical Tomography ◽  
Tunnel Face ◽  
Resistivity Tomography ◽  
Geological Conditions ◽  
Seismic Prediction

AbstractIn deep tunnels with hydro-geological conditions, it is paramount to investigate the geological structure of the region before excavating a tunnel; otherwise, unanticipated accidents may cause serious damage and delay the project. The purpose of this study is to investigate the geological properties ahead of a tunnel face using electrical resistivity tomography (ERT) and tunnel seismic prediction (TSP) methods. During construction of the Nosoud Tunnel located in western Iran, ERT and TSP 303 methods were employed to predict geological conditions ahead of the tunnel face. In this article, the results of applying these methods are discussed. In this case, we have compared the results of the ERT method with those of the TSP 303 method. This work utilizes seismic methods and electrical tomography as two geophysical techniques are able to detect rock properties ahead of a tunnel face. This study shows that although the results of these two methods are in good agreement with each other, the results of TSP 303 are more accurate and higher quality. Also, we believe that using another geophysical method, in addition to TSP 303, could be helpful in making decisions in support of excavation, especially in complicated geological conditions.

Influence of Rock Properties and Geomechanics on Hydraulic Fracturing: A Case Study from the Amin Deep Tight Gas Reservoir, Sultanate of Oman.

2012 ◽  
Author(s):  
Satya V. Perumalla ◽  
Antonio Santagati ◽  
Michael Tony Addis ◽  
Sultan Hamed Al-Mahruqy ◽  
Joe Curtino ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Rock Properties ◽  
Tight Gas ◽  
Sultanate Of Oman ◽  
Gas Reservoir ◽  
Tight Gas Reservoir

scholarly journals Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest

Petroleum ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 304-310
Author(s):  
Ghasem Zargar ◽  
Abbas Ayatizadeh Tanha ◽  
Amirhossein Parizad ◽  
Mehdi Amouri ◽  
Hasan Bagheri
Keyword(s):  
Reservoir Rock ◽  
Rock Properties ◽  
Log Data

scholarly journals An adaptive prediction method for mechanical properties deterioration of sandstone under freeze–thaw cycles: a case study of Yungang Grottoes

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Chenchen Liu ◽  
Yibiao Liu ◽  
Weizhong Ren ◽  
Wenhui Xu ◽  
Simin Cai ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Prediction Method ◽  
Rock Properties ◽  
Gradient Boosting ◽  
Physical Factors ◽  
Freeze Thaw ◽  
Thaw Cycle ◽  
Freeze Thaw Cycle ◽  
Yungang Grottoes

AbstractDue to the location of the Yungang Grottoes, freeze–thaw cycles contribute significantly to the degradation of the mechanical properties of the sandstone. The factors influencing the freeze–thaw cycle are classified into two categories: external environmental conditions and the inherent properties of the rock itself. Since the parameters of rock properties are inherent to each rock, the effect of rock properties on freeze–thaw degradation cannot be investigated by the control variates method. An adaptive multi-output gradient boosting decision trees (AMGBDT) algorithm is proposed to fit nonlinear relationships between mechanical properties and physical factors. The hyperparameters in the GBDT algorithm are set as variables, and the Sequential quadratic programming (SQP) algorithm is applied to solve the hyperparameter optimization, which means finding the maximum Score. The case study illustrates that the AMGBDT algorithm can precisely determine the effect of each independent factor on the output. The patterns of mechanical properties are similar when the number of freeze–thaw cycles and porosity are used as variables separately and when both are used simultaneously. The uniaxial compressive strength decay rate is positively correlated with the number of freeze–thaw cycles and porosity. The modulus of elasticity is negatively correlated with the number of freeze–thaw cycles and porosity. The results show that the number of freeze–thaw cycles is the main factor influencing the freeze–thaw cycling action, and the porosity is minor. In addition, the fitting accuracy of the AMGBDT algorithm is generally higher than neural networks (NN) and random forests (RF). Studying the influence of porosity and other rock properties on the freeze–thaw cycle will help to understand the failure mechanism of rock freeze–thaw cycles.

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